A Highly Durable and UV‐Resistant Graphene‐Based Knitted Textile Sensing Sleeve for Human Joint Angle Monitoring and Gesture Differentiation
Open accessAbstract
Flexible strain sensors based on textiles have attracted extensive attention owing to their light weight, flexibility, and comfort when wearing. However, challenges in integrating textile strain sensors into wearable sensing devices include the need for outstanding sensing performance, long‐term monitoring stability, and fast, convenient integration processes to achieve comprehensive monitoring. The scalable fabrication technique presented here addresses these challenges by incorporating customizable graphene‐based sensing networks into knitted structures, thus creating sensing sleeves for precise motion detection and differentiation. The performance and real‐world application potential of the sensing sleeve are evaluated by its precision in angle estimation and complex joint motion recognition during intra‐ and intersubject studies. For intra‐subject analysis, the sensing sleeve only exhibits a 2.34° angle error in five different knee activities among 20 participants, and the sensing sleeves show up to 94.1% and 96.1% accuracy in the gesture classification of knee and elbow, respectively. For inter‐subject analysis, the sensing sleeve demonstrates a 4.21° angle error, and it shows up to 79.9% and 85.5% accuracy in the gesture classification of knee and elbow, respectively. An activity‐guided user interface compatible with the sensing sleeves for human motion monitoring in home healthcare applications is presented to illustrate the potential applications.
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The combination of joint anatomical structure and waveform features enables dynamic analysis and classification of different gestures.
“More refined motion recognition can even be extended to the hand, where the combination of joint anatomical structure and waveform features enables dynamic analysis and classification of different gestures (Fig. 4g) 62 .”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [62]
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- Authors
- Zhou, Y., Sun, Y., Li, Y., Shen, C., Lou, Z., Min, X., & Stewart, R.
- Affiliations
- Imperial College London (GB)
- Beihang University (CN)
- Jiangnan University (CN)
- Corresponding author
- Zhou, Yi
- Stewart, Rebecca
- Venue
- Advanced Intelligent Systems
- Publisher
- Wiley
- Published
- 2024-06-02
- Volume
- 6
- Issue
- 10
- ISSN
- 2640-4567 (ISSN-L)
- OpenAlex
- W4399276038
- Cited by
- 17
- References
- 56
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 1
- FWCI
- 1.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Advanced Intelligent Systems (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- Funders
- Imperial College London
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- knitted fabrics, gesture recognition, knee joint motion, elbow motion, UV resistance, human motion monitoring, home health care, user interface
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